A privacy mechanism for mobile-based urban traffic monitoring

In mobile-based traffic monitoring applications, each user provides real-time updates on their location and speed while driving. This data is collected by a centralized server and aggregated to provide participants with current traffic conditions. Successful participation in traffic monitoring applications utilizing participatory sensing depends on two factors: the information utility of the estimated traffic condition, and the amount of private information (position and speed) each participant reveals to the server. We assume each user prefers to reveal as little private information as possible, but if everyone withholds information, the quality of traffic estimation will deteriorate. In this paper, we model these opposing requirements by considering each user to have a utility function that combines the benefit of high quality traffic estimates and the cost of privacy loss. Using a novel Markovian model, we mathematically derive a policy that takes into account the mean, variance and correlation of traffic on a given stretch of road and yields the optimal granularity of information revelation to maximize user utility. We validate the effectiveness of this policy through real-world empirical traces collected during the Mobile Century experiment in Northern California. The validation shows that the derived policy yields utilities that are very close to what could be obtained by an oracle scheme with full knowledge of the ground truth.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

Cloud computing adoption in Ghana; accounting for institutional factors

In spite of increasing adoption of Cloud Computing (CC) by organizations in developed countries, the rate of adoption of the technology in sub-Saharan Africa has stagnated, although it has the potential to accelerate the speed of digital transformation. This paper contributes to the extant literature in this light by examining how the institutional environment influences the adoption of cloud computing. We investigated the role of mimetic, coercive and normative institutional pressures in predicting adoption outcomes of cloud computing among organizations in the region. After testing three main hypotheses and ten corollaries, and analyzing data collected from seventy-nine organizations with the partial least square structural equation modelling (PLS-SEM), the study found that the institutional pressures (mimetic, coercive and normative) explain 27% of the variance in cloud computing adoption. The mimetic – CC adoption path coefficient of 0.35 made the biggest contribution to the model while the normative – CC adoption path was the least contributor with a path coefficient of 0.18. The findings explain the determinants of adoption of CC in environments of low adoption and institutional challenges.     

FogMed: A Fog-Based Framework for Disease Prognosis Based Medical Sensor Data Streams

Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However, there are still numerous unresolved issues worth to be explored. For instance, there is a lack of investigation on the disease prediction in fog environment and only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance (e.g., accuracy and time efficiency). To address these issues, we propose a fog-based framework for disease prediction based on Medical sensor data streams, named FogMed. This framework aims to improve the disease prediction accuracy by achieving two objectives: QoS guarantee of fog services and anomaly prediction of Medical data streams. We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed. The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.     

面向云计算应用层演化的隐私保护方法研究

云计算应用层中的组合服务具有演化属性,因此,隐私数据在服务组合过程中,用户的隐私数据可能会因为服务或服务流程的演化而暴露。根据服务演化的特征,以描述逻辑为基础,提出了一种面向云计算应用层演化的隐私保护方法。对隐私协议进行形式化描述;根据服务的演化特征,对服务的演化进行监控,保证满足用户的隐私需求;利用实例研究证明该方法的正确性与可行性。     

基于大数据的智慧林业系统设计

With the development of 5G, big data, cloud computing, IOT, mobile Internet and other information technologies of new generation, the design of forestry information system establishes a new mode of forestry development integrating three-dimensional perception, collaborative management and internal and external services by means of perception, IOT and intelligence. Intelligent forestry system is a comprehensive information management system integrating forest management, intelligent forestry business management, green maintenance management and emergency command and dispatching management. Based on the specific project practice, this paper puts forward the overall framework design scheme and management platform design scheme of intelligent forest- ry.     

Anderson Acceleration for Nonconvex ADMM Based on Douglas-Rachford Splitting

The alternating direction multiplier method (ADMM) is widely used in computer graphics for solving optimization problems that can be nonsmooth and nonconvex. It converges quickly to an approximate solution, but can take a long time to converge to a solution of high-accuracy. Previously, Anderson acceleration has been applied to ADMM, by treating it as a fixed-point iteration for the concatenation of the dual variables and a subset of the primal variables. In this paper, we note that the equivalence between ADMM and Douglas-Rachford splitting reveals that ADMM is in fact a fixed-point iteration in a lower-dimensional space. By applying Anderson acceleration to such lower-dimensional fixed-point iteration, we obtain a more effective approach for accelerating ADMM. We analyze the convergence of the proposed acceleration method on nonconvex problems, and verify its effectiveness on a variety of computer graphics including geometry processing and physical simulation.     

高性能计算分析技术在大型钠冷快堆屏蔽设计中的应用

钠冷快堆堆容器是一体化的池式结构，由众多堆内构件组成且结构复杂，堆芯到生物屏蔽外中子输运过程中各向异性明显且深穿透问题严重，大尺度范围下三维S_N方法计算是制约快堆屏蔽设计的瓶颈。通过将三维SN程序与高性能计算技术相结合，采用并行计算方法可解决快堆堆本体内各向异性的三维深穿透屏蔽问题。本文以中国示范快堆（CFR600）堆本体为研究对象，采用JSNT-CFR程序详细计算了堆本体内的中子注量率、光子注量率、剂量率，并将计算结果与已有的二维程序设计结果进行比较。结果表明，将传统屏蔽计算方法与高性能计算相结合，能满足CFR600堆本体屏蔽计算精度要求，获得更为全面的三维展示效果，在计算模型复杂、粒子穿透深度等复杂问题的屏蔽计算上具有较明显的优势，为大型钠冷快堆屏蔽设计提供有力支撑。     

An architecture for digital hate content reduction with mobile edge computing

Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate content. The objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's level. In this regard, the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative analyses. Profiling resulted in different categories of hate content caused by gender, religion, race, and disability. Based on this information, an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing environment. The proposed architecture will be a novel idea to reduce hate content generation and its impact.     

Distributed privacy protection strategy for MEC enhanced wireless body area networks

With the rapid development and widespread application of Wireless Body Area Networks (WBANs), the traditional centralized system architecture cannot handle the massive data generated by the edge devices. Meanwhile, in order to ensure the security of physiological privacy data and the identity privacy of patients, this paper presents a privacy protection strategy for Mobile Edge Computing(MEC) enhanced WBANs, which leverages the blockchain-based decentralized MEC paradigm to support efficient transmission of privacy information with low latency, high reliability within a high-demand data security scenario. On this basis, the Merkle tree optimization model is designed to authenticate nodes and to verify the source of physiological data. Furthermore, a hybrid signature algorithm is devised to guarantee the node anonymity with unforgeability, data integrity and reduced delay. The security performance analysis and simulation results show that our proposed strategy not only reduces the delay, but also secures the privacy and transmission of sensitive WBANs data.